Module 1: Wrap-Up

Language Probability and Representation

1 What We Learned

In Module 1, we established the conceptual foundation of generative AI by reframing language as a probabilistic modeling problem.

• From Determinism to Probability We examined how large language models generate text through conditional probability rather than rule-based reasoning.

• Reproducible GenAI Workflows You set up structured environments for experimentation and documentation, reinforcing accountability in AI development.

• Prediction as Generation We formalized generation as next-token prediction under uncertainty.

• Uncertainty and Model Behavior Concepts like entropy and conditional likelihood were introduced to explain why outputs vary and why hallucinations occur.

Together, these ideas shift your perspective from “AI as intelligence” to “AI as statistical sequence modeling.”

2 Preparing for Module 2

Module 2 moves from probability to representation and mathematical structure. To prepare:

• Review conditional probability and basic vector concepts.
• Reflect on how meaning could be encoded geometrically.
• Revisit entropy and perplexity as measures of model performance.
• Ensure your experimentation workflow is fully reproducible and documented.

Next, we move from predicting language to representing meaning mathematically.

You now have the theoretical lens required to understand everything that follows.